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---
datasets:
- bigscience/xP3
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
widget:
- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous
review as positive, neutral or negative?
example_title: zh-en sentiment
- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？
example_title: zh-zh sentiment
- text: Suggest at least five related search terms to "Mạng neural nhân tạo".
example_title: vi-en query
- text: Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels».
example_title: fr-fr query
- text: Explain in a sentence in Telugu what is backpropagation in neural networks.
example_title: te-en qa
- text: Why is the sky blue?
example_title: en-en qa
- text: 'Write a fairy tale about a troll saving a princess from a dangerous dragon.
The fairy tale is a masterpiece that has achieved praise worldwide and its moral
is "Heroes Come in All Shapes and Sizes". Story (in Spanish):'
example_title: es-en fable
- text: 'Write a fable about wood elves living in a forest that is suddenly invaded
by ogres. The fable is a masterpiece that has achieved praise worldwide and its
moral is "Violence is the last refuge of the incompetent". Fable (in Hindi):'
example_title: hi-en fable
base_model: bigscience/bloomz-1b7
tags:
- TensorBlock
- GGUF
model-index:
- name: bloomz-1b7
results:
- task:
type: Coreference resolution
dataset:
name: Winogrande XL (xl)
type: winogrande
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics:
- type: Accuracy
value: 51.14
- task:
type: Coreference resolution
dataset:
name: XWinograd (en)
type: Muennighoff/xwinograd
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 56.34
- task:
type: Coreference resolution
dataset:
name: XWinograd (fr)
type: Muennighoff/xwinograd
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 55.42
- task:
type: Coreference resolution
dataset:
name: XWinograd (jp)
type: Muennighoff/xwinograd
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 52.55
- task:
type: Coreference resolution
dataset:
name: XWinograd (pt)
type: Muennighoff/xwinograd
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 53.23
- task:
type: Coreference resolution
dataset:
name: XWinograd (ru)
type: Muennighoff/xwinograd
config: ru
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 55.24
- task:
type: Coreference resolution
dataset:
name: XWinograd (zh)
type: Muennighoff/xwinograd
config: zh
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 56.15
- task:
type: Natural language inference
dataset:
name: ANLI (r1)
type: anli
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 34.0
- task:
type: Natural language inference
dataset:
name: ANLI (r2)
type: anli
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 36.1
- task:
type: Natural language inference
dataset:
name: ANLI (r3)
type: anli
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 37.08
- task:
type: Natural language inference
dataset:
name: SuperGLUE (cb)
type: super_glue
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 71.43
- task:
type: Natural language inference
dataset:
name: SuperGLUE (rte)
type: super_glue
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 76.17
- task:
type: Natural language inference
dataset:
name: XNLI (ar)
type: xnli
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 50.04
- task:
type: Natural language inference
dataset:
name: XNLI (bg)
type: xnli
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 42.17
- task:
type: Natural language inference
dataset:
name: XNLI (de)
type: xnli
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 42.73
- task:
type: Natural language inference
dataset:
name: XNLI (el)
type: xnli
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 41.81
- task:
type: Natural language inference
dataset:
name: XNLI (en)
type: xnli
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 55.02
- task:
type: Natural language inference
dataset:
name: XNLI (es)
type: xnli
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 52.97
- task:
type: Natural language inference
dataset:
name: XNLI (fr)
type: xnli
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 52.21
- task:
type: Natural language inference
dataset:
name: XNLI (hi)
type: xnli
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 48.07
- task:
type: Natural language inference
dataset:
name: XNLI (ru)
type: xnli
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 45.1
- task:
type: Natural language inference
dataset:
name: XNLI (sw)
type: xnli
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 44.34
- task:
type: Natural language inference
dataset:
name: XNLI (th)
type: xnli
config: th
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 40.36
- task:
type: Natural language inference
dataset:
name: XNLI (tr)
type: xnli
config: tr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.15
- task:
type: Natural language inference
dataset:
name: XNLI (ur)
type: xnli
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 44.38
- task:
type: Natural language inference
dataset:
name: XNLI (vi)
type: xnli
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 51.08
- task:
type: Natural language inference
dataset:
name: XNLI (zh)
type: xnli
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 51.12
- task:
type: Program synthesis
dataset:
name: HumanEval
type: openai_humaneval
config: None
split: test
revision: e8dc562f5de170c54b5481011dd9f4fa04845771
metrics:
- type: Pass@1
value: 4.38
- type: Pass@10
value: 8.73
- type: Pass@100
value: 16.09
- task:
type: Sentence completion
dataset:
name: StoryCloze (2016)
type: story_cloze
config: '2016'
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics:
- type: Accuracy
value: 82.9
- task:
type: Sentence completion
dataset:
name: SuperGLUE (copa)
type: super_glue
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 69.0
- task:
type: Sentence completion
dataset:
name: XCOPA (et)
type: xcopa
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 50.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ht)
type: xcopa
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
name: XCOPA (id)
type: xcopa
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 61.0
- task:
type: Sentence completion
dataset:
name: XCOPA (it)
type: xcopa
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 49.0
- task:
type: Sentence completion
dataset:
name: XCOPA (qu)
type: xcopa
config: qu
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 56.0
- task:
type: Sentence completion
dataset:
name: XCOPA (sw)
type: xcopa
config: sw
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 57.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ta)
type: xcopa
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 56.0
- task:
type: Sentence completion
dataset:
name: XCOPA (th)
type: xcopa
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 60.0
- task:
type: Sentence completion
dataset:
name: XCOPA (tr)
type: xcopa
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
name: XCOPA (vi)
type: xcopa
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 70.0
- task:
type: Sentence completion
dataset:
name: XCOPA (zh)
type: xcopa
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 67.0
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ar)
type: Muennighoff/xstory_cloze
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 73.33
- task:
type: Sentence completion
dataset:
name: XStoryCloze (es)
type: Muennighoff/xstory_cloze
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 77.96
- task:
type: Sentence completion
dataset:
name: XStoryCloze (eu)
type: Muennighoff/xstory_cloze
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 60.49
- task:
type: Sentence completion
dataset:
name: XStoryCloze (hi)
type: Muennighoff/xstory_cloze
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 72.87
- task:
type: Sentence completion
dataset:
name: XStoryCloze (id)
type: Muennighoff/xstory_cloze
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 74.92
- task:
type: Sentence completion
dataset:
name: XStoryCloze (my)
type: Muennighoff/xstory_cloze
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 51.09
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ru)
type: Muennighoff/xstory_cloze
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 56.39
- task:
type: Sentence completion
dataset:
name: XStoryCloze (sw)
type: Muennighoff/xstory_cloze
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 61.28
- task:
type: Sentence completion
dataset:
name: XStoryCloze (te)
type: Muennighoff/xstory_cloze
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 66.25
- task:
type: Sentence completion
dataset:
name: XStoryCloze (zh)
type: Muennighoff/xstory_cloze
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 78.69
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## bigscience/bloomz-1b7 - GGUF
This repo contains GGUF format model files for [bigscience/bloomz-1b7](https://huggingface.co/bigscience/bloomz-1b7).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
## Prompt template
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [bloomz-1b7-Q2_K.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q2_K.gguf) | Q2_K | 0.980 GB | smallest, significant quality loss - not recommended for most purposes |
| [bloomz-1b7-Q3_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q3_K_S.gguf) | Q3_K_S | 1.096 GB | very small, high quality loss |
| [bloomz-1b7-Q3_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q3_K_M.gguf) | Q3_K_M | 1.197 GB | very small, high quality loss |
| [bloomz-1b7-Q3_K_L.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q3_K_L.gguf) | Q3_K_L | 1.254 GB | small, substantial quality loss |
| [bloomz-1b7-Q4_0.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q4_0.gguf) | Q4_0 | 1.309 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [bloomz-1b7-Q4_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q4_K_S.gguf) | Q4_K_S | 1.315 GB | small, greater quality loss |
| [bloomz-1b7-Q4_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q4_K_M.gguf) | Q4_K_M | 1.392 GB | medium, balanced quality - recommended |
| [bloomz-1b7-Q5_0.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q5_0.gguf) | Q5_0 | 1.509 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [bloomz-1b7-Q5_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q5_K_S.gguf) | Q5_K_S | 1.509 GB | large, low quality loss - recommended |
| [bloomz-1b7-Q5_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q5_K_M.gguf) | Q5_K_M | 1.571 GB | large, very low quality loss - recommended |
| [bloomz-1b7-Q6_K.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q6_K.gguf) | Q6_K | 1.722 GB | very large, extremely low quality loss |
| [bloomz-1b7-Q8_0.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/tree/main/bloomz-1b7-Q8_0.gguf) | Q8_0 | 2.226 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/bloomz-1b7-GGUF --include "bloomz-1b7-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/bloomz-1b7-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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